This dataset describes the share of children under age 6 in working families that have non-traditional hour (NTH) working parents, by state and children’s personal and family characteristics. This dataset also identifies policies related to NTH child care each state planned to implement according to its 2019-2021 CCDF plan. These data are referenced in the Urban Institute brief: “Comparing Potential Demand for Nontraditional-Hour Child Care and Planned Policies across States.”

This page features National Institute of Standards and Technology Public Safety Communications Research Division 2018 Differential Privacy Synthetic Data Challenge datasets, including original data from marathon matches two and three and the top five placing competitors’ differentially private synthetic datasets. Go to this webpage for further details on the data challenge. The data contain only a subset of the possible variables, and the Public Use Microdata Sample (PUMS) is 1940 Decennial Census data. Match 2 data are the San Francisco Fire Department’s Call for Service Data, which can be freely downloaded here. Match 3 data are the US Census Bureau public use micro sample (PUMS) files of Arizona and Vermont.

About the Safety Net Almanac Data These datasets were used to support the data visualizations for the Urban Institute's Safety Net Almanac . The Safety Net Almanac brings together information about government programs that serve people in need. The Safety Net Almanac features summaries of each program, legislative histories, and interactive graphics that illustrate who's receiving help and what the costs are for states and the federal government.. The Almanac includes eight major programs serving low-income families and individuals. Supplemental Nutrition Assistance Program (SNAP) Temporary Assistance for Needy Families (TANF) Earned Income Tax Credit (EITC) and Child Tax Credit (CTC) Supplemental Security Income (SSI) Medicaid and CHIP Child care assistance Housing assistance Unemployment Insurance Data for the different programs have been stored in a common format described in the "data dictionary fields" sheet. The reference table "statistics_ref" gives information on all statistics stored in the database. Statistics are time series, stored in various time frames within 1969 – 2012 (current release), at national and state levels. Each statistic has a unique "StatID," and statistics are grouped by programs (as defined in the "programs" section of the data dictionary spreadsheet).

This dataset presents statistics on the credit health of student loan borrowers, derived from a representative sample of US adults with a credit record as recorded by one of the three major credit bureaus.

The data file includes every variable based on the original 70-question survey instrument, as well as constructed variables used in analysis for an upcoming report. For each variable, its label is offered, the values it contains, means (unweighted and weighted) for continuous numerical values, and frequencies and percentages (unweighted and weighted) for categorical values. Identifiers are not included.

Data Last Updated 2020-07-30 Note: This entry includes the data for the first phase of the Household Pulse Survey, which ran from April 23 to July 21, 2020. For the Household Pulse Survey data from phase 2 onward, please see the entry here. The Urban Institute has automated the process of reading in and cleaning the Household Pulse Survey Public Use Microdata files. We have combined the available public use files across all weeks into one standardized CSV (Public Use File All Weeks.csv) and generated a rolling two week summary file (Two Week Rolling Avg Pulse Metrics.csv). that powers the Tracking COVID-19’s Effects by Race and Ethnicity feature. These datasets will be updated weekly as new public use files are released. Below are descriptions of all available files. To see the code for how we generated these data files, please visit our Github repo. - Public Use Files All Weeks.csv: A compiled CSV of public use files from all available weeks.. The file structure will be very similar to the Census Bureau’s published files but will have a few new columns appended. Each row represents a respondent from a given week. Please carefully note the pweight column which is the person weight assigned by the Census Bureau to each respondent. Any metric you create from the data has to sum across the pweight column to arrive at the individual level estimates. For additional help understanding what each of the columns in this file represent, please see the data dictionary files below. - PUF Census Data Dictionary.: The data dictionary as provided by the Census Bureau for the latest week in the data. This will be exactly the same as the Census Bureau's published data dictionary files. Note a couple of the Urban appended columns will not be in the data dictionary. For definitions of the appended columns, please see the PUF Urban Data Dictionary file - PUF Urban Data Dictionary: Urban has appended a few columns to the publicly available public use file. This file contains the descriptions of those appended columns. This file in conjunction with the PUF Census Data Dictionary should allow you to understand what each column means in the Public Use File All Weeks file. - Two Week Rolling Avg Pulse Metrics: This is a two week rolling average summary file of a few selected metrics from the Household Pulse Survey. Each row in this file represents a indicator/geography/week/race combination. For more information on what each of the columns in this file represent, see the Two Week Rolling Avg Data Dictionary. - Two Week Rolling Avg Data Dictionary: The data dictionary for the above Two Week Rolling Avg Pulse Metrics dataset. If you have any questions, please reach out to astern@urban.org

This dataset contains statistics presented in the Tracking Resident Credit Health During COVID-19 project. These statistics are based on a 2 percent nationally representative panel of deidentified, consumer-level records from a major credit bureau. The credit bureau data are from August 2010 to October 2020 and contain data on more than five million consumers. Data from 2010 to 2019 are collected annually in August. Data from 2020 were collected every two months starting in February.

This page features National Institute of Standards and Technology Public Safety Communications Research Division 2018 Differential Privacy Synthetic Data Challenge datasets, including original data from marathon matches two and three and the top five placing competitors’ differentially private synthetic datasets. Go to this webpage for further details on the data challenge. The data contain only a subset of the possible variables, and the Public Use Microdata Sample (PUMS) is 1940 Decennial Census data. Match 2 data are the San Francisco Fire Department’s Call for Service Data, which can be freely downloaded here. Match 3 data are the US Census Bureau public use micro sample (PUMS) files of Arizona and Vermont.

The AFDC Data Archive contains Aid to Families with Dependent Children (AFDC) characteristics files from 1967, 1969, 1971, 1973, 1975, 1977, and 1979, as well as AFDC quality control data from 1983–1997. The archive was funded by the Office of the Assistant Secretary for Planning and Evaluation (ASPE) in the Department of Health and Human Services (HHS). It was developed by staff of the Urban Institute. In the past, these data were primarily housed on an HHS/ASPE website at https://aspe.hhs.gov/afdc-data-archive .

The National Survey of America's Families (NSAF) is a household survey that provides a comprehensive look at quantitative measures of the well-being of children, adults, and their families. While the focus of the survey is at the state level, the scope is national, with a primary emphasis on low-income families. NSAF information was gathered from interviews conducted with the most knowledgeable adult (MKA), the person in the household who was most knowledgeable about the questions being asked about the respondent, their spouse/partner (if applicable) and the focal child (or children).

These are the data behind Measuring Upward Mobility in Counties and Cities Across the US. Read the Data Dictionary to learn more about the data files.

This dataset combines public data from the Integrated Postsecondary Education Data System and the US Census Bureau’s American Community Service in an index of racial and ethnic representativeness of US postsecondary education institutions. The data link college racial composition to the racial composition of an institution’s “market,” defined geographically according to institutions’ level, degree of selectivity, and urbanicity.

This dataset presents federal and state funding estimates by state for two free community college programs: (1) America's College Promise detailed in the House Committee on Education and Labor print providing for reconciliation pursuant to S. Con. Res. 14 (117th Congress) and (2) an alternative program that works under a similar framework. You can find more information about these estimates in our blog post.

These are the data behind Measuring Upward Mobility in Counties and Cities Across the US. Read the Data Dictionary to learn more about the data files.

These are the data behind Measuring Upward Mobility in Counties and Cities Across the US. Read the Data Dictionary to learn more about the data files.

Updated 2021-04-15 This entry includes the data behind the Urban Institute's Unequal Commute feature which was published on October 6, 2020. For more information on how the data included here is calculated, please reference our technical appendix. Below are descriptions of all available files: * job_access_gap.geojson: A geojson file that includes metrics on access to jobs at the block group level and geospatial data on blockgroup boundaries that is used to create the feature maps. * job_access_gap.csv: A csv file that includes the same fields as the geojson file above. * job_access_dict.csv: Data dictionary for job_access_gap.geojson and job_access_gap.csv. * job_access_gap_equity.csv: A csv file that includes racial and ethnic composition of block groups with the highest 10% of spatial mismatch in each CBSA and overall racial and ethnic composition in each CBSA. This file is used to create the feature bar plot. * job_access_gap_equity_dict.csv: Data dictionary for job_access_gap_equity.csv. If you have any questions, please reach out to astern@urban.org.

We analyzed investment in the Washington, DC, region between 2010 and 2020 in the asset classes listed below. We define the Washington, DC, region as the following jurisdictions: Alexandria City; Arlington County; Fairfax City; Fairfax County; Falls Church City; Loudon County; Montgomery County; Prince George’s County; and Washington, DC. The unit of analysis is a census tract, using 2010 boundaries, and some tables are aggregated to the city level. These data are inflation-adjusted to 2021 dollars.

This dataset describes the average cost per moderately priced meal, the maximum total Supplemental Nutrition Assistance Program (SNAP) benefits (before and after the 15 percent increase in 2020), and the gap between meal cost and benefits for all US counties.

These datasets contain state Medicaid and national Medicare fees for a weekly Opioid Treatment Program methadone bundle and its component services, a bundle with additional services, and selected other services related to the treatment of opioid use disorder, with indexes for interstate comparison of Medicaid reimbursement and intrastate comparison of Medicaid versus Medicare reimbursement.